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Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication‐constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH‐RIS. EH‐RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone‐RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone‐RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL‐based EH‐RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone‐RIS spectrum‐efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH‐RIS work together to optimize drone‐RIS for next‐generation wireless networks. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of traditional date palm harvesting, which is often labor-intensive and hazardous, by introducing an innovative solution utilizing multirotor flying vehicles (MRFVs). Unlike conventional methods such as hydraulic lifts and ground-based robotic manipulators, the proposed system integrates a quadrotor equipped with a winch and a suspended robotic arm with a precision saw. Controlled remotely via a mobile application, the quadrotor navigates to targeted branches on the date palm tree, where the robotic arm, guided by live video feedback from integrated cameras, accurately severs the branches. Extensive testing in a controlled environment demonstrates the system's potential to significantly improve harvesting efficiency, safety, and cost-effectiveness. This approach offers a promising alternative to traditional harvesting methods, providing a scalable solution for date palm cultivation, particularly in regions with large-scale plantations. This work marks a significant advancement in the field of agricultural automation, offering a safer, more efficient method for harvesting date palms and contributing to the growing body of knowledge in automated farming technologies. [ABSTRACT FROM AUTHOR]

The purpose of this demonstration study was to determine the effect of prior experiences and gender on cognitive demands at the initial educational technology experience. The task of drone operation was selected as a novel educational technological tool and videogame use was determined to be a tangential prior experience. Through the framework of embodied cognition, the NASA Task Load Index was used to measure cognitive demand. Practicing middle- and high-school science teachers were recruited to participate in this study. Participants operated drones at three instances over two weeks. An additional challenging task was also presented. A mixed ANOVA suggested a significant interaction between videogame use and time: F(2, 28) = 3.77, p < 0.05. A significant interaction between gender and time was also observed: F(2, 29) = 3.72, p < 0.05. For both prior experience and gender, the differences were observed at the first instance of drone operation and not at subsequent trials. Results suggest prior experience and gender may only affect cognitive load at the initial experience and leveling of cognitive load occurs with continued practice. These findings can support an educator's planning for drone-based activities and help to mitigate the stigma of gender differences regarding technology use. Additionally, these findings support the need to examine and enhance related and transferable skills, such as hand–eye coordination and spatial thinking, to lessen cognitive load at the initial educational experience. [ABSTRACT FROM AUTHOR]

In coastal British Columbia, Canada, marine megafauna such as humpback whales (Megaptera novaeangliae) and fin whales (Balaenoptera physalus velifera) have been subject to a history of exploitation and near extirpation. While their populations have been in recovery, significant threats are posed to these vulnerable species by proposed natural resource ventures in this region, in addition to the compounding effects of anthropogenic climate change. Genetic tools play a vital role in informing conservation efforts, but the associated collection of tissue biopsy samples can be challenging for the investigators and disruptive to the ongoing behaviour of the targeted whales. Here, we evaluate a minimally intrusive approach based on collecting exhaled breath condensate, or respiratory 'blow' samples, from baleen whales using an unoccupied aerial system (UAS), within Gitga'at First Nation territory for conservation genetics. Minimal behavioural responses to the sampling technique were observed, with no response detected 87% of the time (of 112 UAS deployments). DNA from whale blow (n = 88 samples) was extracted, and DNA profiles consisting of 10 nuclear microsatellite loci, sex identification and mitochondrial (mt) DNA haplotypes were constructed. An average of 7.5 microsatellite loci per individual were successfully genotyped. The success rates for mtDNA and sex assignment were 80% and 89% respectively. Thus, this minimally intrusive sampling method can be used to describe genetic diversity and generate genetic profiles for individual identification. The results of this research demonstrate the potential of UAS‐collected whale blow for conservation genetics from a remote location. see also the Perspective by Elena Valsecchi. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat to ecosystems, property, and human life, making their early and accurate detection crucial for effective intervention. This study presents a novel, lightweight approach to real-time forest fire detection that is optimized for resource-constrained devices like drones. The method integrates multi-task knowledge distillation, transferring knowledge from a high-performance DenseNet201 teacher model that was trained on a hierarchically structured wildfire dataset. The dataset comprised primary classes (fire vs. non-fire) and detailed subclasses that account for confounding elements such as smoke, fog, and reflections. The novelty of this approach lies in leveraging knowledge distillation to transfer the deeper insights learned by the DenseNet201 teacher model—specifically, the auxiliary task of recognizing the confounding elements responsible for false positives—into a lightweight student model, enabling it to achieve a similar robustness without the need for complex architectures. Using this distilled knowledge, we trained a MobileNetV3-based student model, which was designed to operate efficiently in real-time while maintaining a low computational overhead. To address the challenge of false positives caused by visually similar non-fire elements, we introduced the Confounding Element Specificity (CES) metric. This novel metric, made possible by the hierarchical structure of the wildfire dataset, is unique in its focus on evaluating how well the model distinguishes actual fires from the confounding elements that typically result in false positives within the negative class. The proposed approach outperformed the baseline methods—including single-task learning and direct multi-task learning—achieving a primary accuracy of 93.36%, an F1-score of 91.57%, and a higher MES score, demonstrating its enhanced robustness and reliability in diverse environmental conditions. This work bridges the gap between advanced deep learning techniques and practical, scalable solutions for environmental monitoring. Future research will focus on integrating multi-modal data and developing adaptive distillation techniques to further enhance the model's performance in real-time applications. [ABSTRACT FROM AUTHOR]

Body condition is a measure of an animal's energy reserves relative to its body structure that provides important information about individual- and population-level health. Monitoring the body condition of free-ranging cetaceans has historically been difficult, but in recent years, the unmanned aerial system (UAS, or "drone") has facilitated noninvasive ways of estimating the cetacean body condition. The Charleston Estuarine System (CES) includes the estuarine and coastal ecosystems surrounding Charleston, South Carolina, and is utilized by Tamanend's bottlenose dolphins (Tursiops erebennus) throughout the year. The main goals of this study were (1) to test if UASs are suitable for monitoring body condition of dolphins in an estuarine environment, and (2) to determine if site, season, and age class influence the body condition of dolphins in the CES. Land-based UAS surveys were conducted at four sites throughout the CES between September 2022 and May 2023. The body condition of each dolphin was evaluated using images of the individual positioned flat with a straight body at the surface, and a linear mixed effects model was constructed to determine which effects were associated with significant differences in dolphin body condition. After filtering images for quality, 428 images of 174 unique dolphins were included in the final analysis, with repeated body condition estimates of 24 dolphins from multiple seasons. Both season and age class were significant predictors of dolphin body condition, but site was not. In addition, individual dolphins were catalogued in a Drone Dolphin ID database, which allowed some dolphins' unique body condition changes to be tracked over time. These findings provide an important baseline for dolphin body condition in the CES that can be built upon in future studies to better understand how body condition changes in response to environmental and anthropogenic stressors or for different age classes. [ABSTRACT FROM AUTHOR]

Highly pathogenic avian influenza has emerged as a global menace over the past decades, posing a dramatic threat to birds worldwide, and its spread is of great concern to both conservationists and health authorities. In the 2022 breeding season, the Sandwich Tern Thalasseus sandvicensis was affected by a northwestern Europe-wide pandemic of the highly pathogenic avian influenza A (strain H5N1). This note reports on the occurrence of avian influenza in Sandwich Terns breeding in the Lagoon of Venice (Italy) in 2023 where the outbreak exterminated all chicks, but spared the adults. [ABSTRACT FROM AUTHOR]

Directed energy weapons represent an emerging, disruptive, credible, relevant technology with the potential for use in all military action domains and with the role of influencing the power balance globally. Given that the nature of the threats has changed in the current security environment, the great powers need a more strategic approach to capitalize on future technologies, including directed energy weapons, in a successful manner. Against the background of escalating tensions in many regions of the globe, emerging technologies are an essential lever for both defence and deterrence. In relation to conventional kinetic weapons, directed energy weapons have some advantages - precision, reduced costs, calibrated effects, possibility to be employed to neutralize vital targets for adversaries without causing collateral damages -, but also some disadvantages: decreasing the force and effect of the radius/beam, relatively range short, impossibility of being employed against the targets located behind some obstacles, low progress in adapting to the needs of the armed forces services and fielding in weapon systems. Electromagnetic radiation is critical for communications, command and control, navigation, targets acquisition, information collection, surveillance, reconnaissance, missile guidance, early warning, and neutralization of targets, aspects that have propelled the development of capabilities, including directed energy weapons directed, to counteract efficient use of electromagnetic spectrum or to neutralize some targets. [ABSTRACT FROM AUTHOR]

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The pressing of hybrid composites with specified properties best meeting the needs of pilotless aircraft (drones) is considered. The proposed model of this process adequately reflects the proportions of the components in the composite and the pressing conditions (pressure, polymerization temperature, and holding time). On the basis of experiments, the characteristics of the hybrid composite (containing glass and carbon fibers) are expressed as a function of the proportion of carbon fibers and the influence of the pressing conditions on the properties of the material is established. [ABSTRACT FROM AUTHOR]

The vehicle routing problem is common for drone delivery in urban areas, and the routing strategies can impact both transport efficiency and community noise. In this article, we propose an optimization method for drone routing to reduce the en route noise impact during the delivery process. A hybrid cost function is adopted to evaluate the quality of the routing strategies, considering both the noise impact and path length. The en route noise is evaluated based on an efficient flight simulation and noise assessment platform, which enables accurate sound source modeling for a practical delivery drone and sound propagation computation in a realistic urban environment. A three-phase heuristic algorithm is employed to optimize the routing strategy. Large-scale simulations conducted in a representative metropolitan area demonstrate significant reductions in both instantaneous and accumulated noise levels. This article highlights the importance of incorporating noise optimization strategies in urban drone delivery systems to improve public acceptance and sustainable air transport. [ABSTRACT FROM AUTHOR]

This study aimed to develop a laboratory method that allows the in vitro maintenance of honey bee drones for several days while preserving their reproductive capacity and to create a new open-source software for the automatic analysis of their sperm quality. Three experiments were performed. The first experiment was designed to validate the new open-source software named CASABee for sperm quality assessment specifically designed for the honey bee. The software was able to identify motile and static spermatozoa with high precision. Results showed a high correlation between the results of sperm quality obtained both manually and by the CASABee system (0.95 and 0.96 for sperm motility and concentration, respectively, p < 0.001). In the second and third experiments, the effect of in vitro maintenance of drones without attendant workers for four days on their ejaculatory capacity and sperm quality, respectively, was evaluated. Survival rate was 98.68%, 89.48%, 75.93%, and 60.97% on average on days 1, 2, 3, and 4 after capturing, respectively. A high proportion of the drones (80.37% on average) were able to ejaculate providing semen, and there were no significant differences in the ejaculatory capacity and sperm quality of drones on the different days of in vitro maintenance, except for sperm viability, which decreased slightly on day 4 (71.71% vs 82.8% on day 0, p < 0.05). It was concluded that the new CASABee system and the method for laboratory maintenance of honey bee drones facilitate the study of reproduction in this species. [ABSTRACT FROM AUTHOR]

Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and public managers to make informed decisions regarding crop health, yield estimation, and resource allocation in Midwest Brazil. The crops (corn) in this region are being damaged by wild pigs and other diseases. For the quantification of corn fields, this paper applies novel computer-vision techniques and a new dataset of corn imagery composed of 1416 256 × 256 images and corresponding labels. We flew nine drone missions and classified wild pig damage in ten orthomosaics in different stages of growth using semi-automatic digitizing and deep-learning techniques. The period of crop-development analysis will range from early sprouting to the start of the drying phase. The objective of segmentation is to transform or simplify the representation of an image, making it more meaningful and easier to interpret. For the objective class, corn achieved an IoU of 77.92%, and for background 83.25%, using DeepLabV3+ architecture, 78.81% for corn, and 83.73% for background using SegFormer architecture. For the objective class, the accuracy metrics were achieved at 86.88% and for background 91.41% using DeepLabV3+, 88.14% for the objective, and 91.15% for background using SegFormer. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Context: Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details of heterogeneous landscapes. The use of unoccupied aerial systems (UAS) to produce high spatial resolution rangeland fractional cover maps could fill that gap at local scales. Objectives: We evaluated the capabilities of UAS imagery for mapping rangeland fractional vegetation cover in sagebrush steppe communities of the Northern Great Basin, USA. Methods: We applied segmentation and machine learning models for image classification, and established regression functions with field-measured herbaceous cover and multiple spectral indices to quantify herbaceous fraction in bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis to compare UAS-derived rangeland fractional cover with satellite-derived products. Results: Overall classification accuracies for the UAS-derived rangeland fractional cover maps were high (89–98%). Modified Soil Adjusted Vegetation Index was the most important spectral index for predicting photosynthetic classes and including Brightness Index in a multiple index approach improved classification of shadows and bare ground. Regression models effectively estimated herbaceous fractions within bare/herbaceous mixed polygons with high accuracy (R2 = 0.71–0.88). UAS-derived rangeland fractional cover estimates captured within-site variability, while satellite-derived products did not, specifically for herbaceous and litter. Conclusions: This study demonstrated a workflow using UAS and intensive ground sampling for estimating rangeland fractional cover in sagebrush communities. We found a disagreement between UAS-derived and satellite-derived fractional cover products at two sagebrush communities in the Northern Great Basin. We recommend the application of UAS when estimating rangeland fractional cover at local scales. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs), commonly known as drones, show great potential in advancing the United Nations (UN) Sustainable Development Goals (SDGs), a collection of 17 worldwide targets outlined by the United Nations (UN) to tackle diverse social, economic, and environmental issues and foster sustainable development by 2030. As per the Federal Aviation Administration (FAA), UAVs can significantly improve areas of sustainability like wildlife and wildfire monitoring, precision agriculture, as well as healthcare. Furthermore, drones with communication capabilities are seen as key elements of non-terrestrial networks, and as enabling technologies for future 6G communication networks. This systematic literature review focuses on the role of UAV-enabled communications to achieve the SDGs. Our study is based on a thorough selection and analysis of scientific articles. Initially, we performed a broad search across three major databases: IEEE Xplore, Web of Science, and Scopus, yielding a set of 10,499 papers. Following a strict selection methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis - PRISMA), we narrowed this set down to 28 key journal articles. Our analysis highlights six global targets that greatly benefit from UAV-enabled aerial communication. In order of impact, these are: "Sustainable Cities and Communities," where UAVs improve security and disaster management; "Life on Land," where drones help with wildlife monitoring and reforestation; "Industry, Innovation, and Infrastructure," where UAVs (Unmanned Aerial Vehicles) aid in infrastructure inspection; "No Hunger," where drones improve agricultural practices; "Good Health and Wellbeing," where UAVs assist with disease prediction and monitoring; and "Clean water and sanitation," where drones play a crucial role in efficiently monitoring water quality and distribution. In summary, this paper offers a detailed look at the applications of UAVs in achieving the SDGs through aerial communications. By identifying areas of major impact and spotting gaps and weaknesses in current research, this review article aims to guide future research and policy-making in sustainable drone applications. [ABSTRACT FROM AUTHOR]

Preliminary analysis in the field of information security in commercial UAV shows significant lacklustre in the implementation of information security [ 1 , 2 ]. This subsequently has made UAV to be especially vulnerable to information attack such as a simple Denial-of-Service (DOS) attack as it is sufficient to severely cripple many of the core functions of commercial UAVs. This research paper aims to provide recommendations in securing UAV from DOS attack works within UAV and proposed solutions against DOS attack. Such as general security guidelines of using long password with mix of special characters; implementing intrusion detection system within the intermediary devices between the connection of the UAV and the pilot, along with proper up-to-date firmware with patches installed; Implementation of an algorithm of a watch dog timer for proper allocation of resources and priority between the processing of network connection attempt and navigational control; Usage of a Raspberry Pi as an intermediary to router for a secure network connection by implementing intrusion detection system to alert the pilot in the event of an attack, and implement the moving target defense technique to hinder the adversaries attempting DOS attack. [ABSTRACT FROM AUTHOR]

Monitoring methods, such as seabed bottom-towed cameras, sediment grabs, and benthic sledges, have limitations in spatial coverage, cause seabed disturbance, are restricted to soft-bottom substrates, and offer low flexibility for marine seabed monitoring. In this study, we investigate the potential of a non-invasive and simple underwater remotely operated vehicle (ROV) to enhance marine seabed monitoring. A tethered ROV equipped with a GoPro camera was deployed in three areas of Skagerrak at depths from 15-34 m to assess accuracy in species identification and substrate classification identified from still frames. The quality of still frames varied between areas due to turbidity, motion blur, and marine snow, which reduced the number of high-quality frames by approximately 20%. Classification of substrates and taxa identification were possible in the remaining still frames. Two different substrates were detected: sand and stone reef. Stone reefs had a lower occurrence compared to sand. A total of 10 taxa were detected in the two substrate types. The highest abundance was observed in the stone reef substrate compared to the sand substrate. Identification at the species level was limited due to the quality of the still frames, which affected the detectability of morphological traits. This study demonstrates that a widely accessible ROV can be used for marine monitoring. The ROV can be used in different substrates, and still frames provide valuable information on species composition, which can enhance the replicability of monitoring programs. [ABSTRACT FROM AUTHOR]

Coastlines are suffering from the effects of erosive processes, the decrease in sediment supply, the rise in mean sea level, and the construction of coastal infrastructure and drainage works, which are further exacerbated by global climate change. The area of the Parque Natural do Litoral Norte (North Coast Natural Park) reveals worsening erosion rates and the transformations directly affect the natural resources that support tourism activities, particularly beach and nature tourism. As part of the CLICTOUR project, we have selected the coastline from Restinga de Ofir to Bonança Beach as a case study. The ESRI ArcGIS software and the Digital Shoreline Analysis System (DSAS) were used to quantify coastline migration and identify the impacts on beach morphology between 2010 and 2023. Based on this information, we calculated changes in carrying capacity and scenarios for visitor usage availability to ensure the protection of fauna and flora, as well as the safety of beachgoers. The results of the linear regression rate confirm the coastline has retreated during the period analyzed (2010–2023). The outcome of these dynamics is noticeable in the beach area, promoting its reduction in area and leisure quality. Considering climate change, this study shows the importance of developing resilience strategies for coastal territories that serve as traditional summer destinations. [ABSTRACT FROM AUTHOR]

Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system's performance. The model is parameterizable, allowing adjustments to the components' resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed. [ABSTRACT FROM AUTHOR]

Recent ground-breaking research in the Internet of Things (IoT) and drone technology has brought about revolutionary advancements in automation, remote sensing, and remote operations. Drones have gained massive popularity in IoT applications, like remote monitoring and task automation. Consequently, an innovative business model called Drones-as-a-Service (DaaS), which aims at leasing out drones for commercial use, is gaining momentum in the market. This spike in demand for drones is credited to their ease of deployment, flexibility of operation, and risk-free functioning. To fully utilize the advantages that drones provide and further delve into the flourishing field of drone research, it is essential to acquaint ourselves with the scope of this field and understand the capabilities of a drone. The methodology employed in this survey paper encompassed a thorough literature review to establish a foundation, followed by the design and implementation of a comprehensive survey instrument. As a result, this survey provides a means for the reader to explore diverse drone research topics, ranging from basic drone types and deployment architectures to complex drone networking and communication systems. Intelligence-based enhancements for drone communications and Blockchain-based security solutions have also been discussed. We have conducted detailed comparisons between different classes and architectures of drones, drone communication protocols (WiFi, LTE, 5G, satellites), and drone-enabled 5G networks (Ad hoc networks and Software Defined Networks). Blockchain-based and Intelligence-assisted solutions for 5G-related drone network security concerns have also been discussed. We also highlight the utility of drones in present-day Smart City Infrastructure, ranging across multitudes of applications, as well as in the Precision Agriculture domain and their future scope. Finally, we outline significant security challenges and cyber-attacks faced by drones and their countermeasures, as proposed in the state-of-the-art literature. In conclusion, we end this survey by discussing the future scope and directions of drone research. [ABSTRACT FROM AUTHOR]

The real‐time use of drone‐derived orthoimagery and ground‐penetrating radar (GPR) facilitate conjunctive ground surveying and aerial visual reference of subterranean features within cemeteries. Geospatially referenced visual outputs allow sympathetic restoration and assist in understanding historical use. Two contrasting case studies demonstrate this: The first is a subterranean wall, built to separate Catholic and Protestant burials in a civic cemetery. The second example is the accurate positioning of unmarked inhumations in an Irish Famine burial ground, which facilitated preservation as a memorial garden in a hospital complex. [ABSTRACT FROM AUTHOR]

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable. [ABSTRACT FROM AUTHOR]

This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 300 m to 5 km, collected using a DJI-M600 hexacopter drone and the National Research Council (NRC) Bell412 Advanced Systems Research Aircraft (ASRA). The dataset consists of hardware-synchronized monocular images, inertial measurement unit (IMU) measurements, 3D light detection and ranging (LiDAR) point-clouds, and high-precision real-time kinematic (RTK)-GNSS based ground truth. Nine data sequences were collected as robot operating system (ROS) bags over 100 mins of outdoor environment footage ranging from urban areas, highways, airports, hillsides, prairies, and waterfronts. The dataset was collected to facilitate the development of visual-inertial-LiDAR odometry and mapping algorithms, visual-inertial navigation algorithms, object detection, segmentation, and landing zone detection algorithms based on real-world drone and full-scale helicopter data. All the data sequences contain raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth. The intrinsic and extrinsic calibrations of the sensors are also provided, along with raw calibration datasets. A performance summary of state-of-the-art methods applied on the data sequences is also provided. [ABSTRACT FROM AUTHOR]

Commercial drone advancements have enabled terrorists with crude airpower, challenging states' aerial dominance. Today, many groups skillfully use drones for propaganda generation, surveillance and reconnaissance, command and control, and attacks. Despite their obvious value, there is wide variation in adoption begging questions about who is using drones and why. Prominent in practitioner and security provider circles, academics are just skimming the surface of this important phenomenon. The small existing literature suggests that violent nonstate actor drone use is little more than Iran-sponsored jihadist terrorists with territory in the Middle East. Using an original data set on characteristics across 998 armed nonstate groups from 1995 to 2019, we explore the empirical determinants of drone adoption. Although Iran-sponsorship is a significant factor, we find that network affiliations are the strongest predictors of adopting a drone program. We also demonstrate that groups with more intensive attack profiles and narco groups are more likely to pursue unmanned aerial systems. Our study provides the first quantitative probe of the drivers of armed nonstate drone use, putting academic assertions and policy prescriptions on firmer empirical ground. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features. [ABSTRACT FROM AUTHOR]

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use. [ABSTRACT FROM AUTHOR]

Basking sharks (Cetorhinus maximus) seasonally aggregate in coastal surface waters of the North Atlantic, providing opportunities for visual observation. While putative courtship displays have been observed, actual copulation has not been documented. Here we examine video collected by an unmanned aerial vehicle ("drone") of novel behavioral interactions between basking sharks in Cape Cod Bay, Massachusetts in May 2021. The behaviors, including close following and tight concentric circling, are consistent with pre‐copulatory behavior observed in other shark species. These observations provide new insights into the pre‐copulatory behavior of basking sharks. [ABSTRACT FROM AUTHOR]

Conservation initiatives such as the Key Biodiversity Areas (KBA) Programme use standardized criteria based on estimates of species abundance to identify critical habitats. They therefore depend on accurate estimates of population sizes for target species. It is essential that the metrics used to measure abundance at a candidate site are consistent with those used to estimate total abundance at national or global scales, because only then can it be determined whether abundance at a site meets threshold criteria. Imagery gathered by remotely piloted aircraft systems (RPAS, or drones) has rapidly become a tool for determining abundance of surface-nesting seabirds and, therefore, can assist with the designation of KBAs. However, abundance data derived from drone imagery are often in different units, such as numbers of birds or numbers of incubating adults visible on photographs, than data derived from in-person counts, which generally measure the number of nests or breeding pairs. Therefore, drone data may not be directly comparable to data that have been historically collected to estimate overall breeding-population sizes. This study considered a candidate colony of Glaucous-winged Gulls Larus glaucescens located in the Salish Sea in southwestern Canada, which has been surveyed both by drone and by traditional ground surveys. We developed a conversion factor that at least partially translates counts of incubating Glaucous-winged Gulls detected on drone imagery to an estimate of breeding pairs. Compensating for only nests without incubating adults, results suggest that numbers of incubating adults detected by drone likely represent between 63% and 84% of the total number of breeding pairs. Applying this conversion increased the population estimate for the colony and changed former conclusions about whether the site met recommended criteria for designation as a national or global KBA for Glaucous-winged Gulls. [ABSTRACT FROM AUTHOR]

In Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War, the authors recount the efforts made by the US Department of Defense beginning in 2015 to engage with the commercial-technology sector by opening a new office, Defense Innovation Unit Experimental (DIUx), in Silicon Valley. The authors led DIUx from 2016 to 2018 and broke from established Pentagon acquisition practices to make cutting-edge technology developed in the private sector available to soldiers and military civilians. Despite early failures and intense bureaucratic resistance, the office eventually secured significant funding and delivered key innovations, including drone-hacking techniques and cloud-penetrating radar satellites. The book emphasises ongoing tensions between traditional military-procurement practices and the need for rapid technological adaptation, using the Pentagon's response to the war in Ukraine as an example and considering how it may respond to advances in artificial intelligence. [ABSTRACT FROM AUTHOR]

Autonomous weapon systems have been used in armed conflicts for the past twenty years. The first country in the world to use them was the United States. Namely, they have proven to be a very effective tool in the fight against terrorism, but also a very effective means of warfare. In recent years, other states have been using them as well. For instance, Azerbaijan was using operationaltactical drones in the Second War against Armenia and was the first state to win a convincing victory by employing them. At present, drones are also being used in the Russo-Ukrainian war. Many world governments are showing massive interest in purchasing them, as well as other types of autonomous weapon systems, for military-strategic advantages. However, it is not only states that are acquiring that kind of weapons. Terrorist organizations, as well as other non-state actors, do so as well. The international community is divided on the issue of permissibility of the use of autonomous weapons. Some states require an absolute ban on their employment, while others do not entirely oppose them, but propose the adoption of an internationally binding agreement to regulate their use. As there is yet no such binding agreement, their permissibility should at the present moment be assessed by examining their compatibility with the existing principles of international humanitarian law. This paper aims at demonstrating that autonomous weapon systems do not meet the requirements of the IHL principles, although development of such weapons is still in progress and is yet to be seen whether this development will lead to a different conclusion. [ABSTRACT FROM AUTHOR]

In recent years, the agriculture industry has witnessed a significant transformation with the integration of cutting-edge technologies to enhance productivity, efficiency, and sustainability. Among these technologies, sensors have emerged as indispensable tools for monitoring various agricultural parameters and optimizing farming practices. Current advancements and technological improvement in the sector of agriculture requires modern day machineries-drones. Drones technologies proved to save the water, pesticides, and herbicides, maintain the soil fertility also helps to cut excess implication of man power along with increase the output and raise the standard of quality. Robots, artificial intelligence, big data, robotics, and the internet of things are all combined in UAVs. Agricultural UAVs have shown to be quite effective when in use, and they are now extensively employed in many facets of agriculture, such as seed sowing, growth evaluation, and mapping, as well as the application of pesticides and fertilizers. It is becoming essential in the field of agronomical research, they are quite susceptible to variation in soil, climate and alteration to physicochemical changes. The aim of this paper is to review the usage of Drones, UAVs and remote sensing in agriculture applications. On the basis of literature, we found that a lot of advantages are witnessed in agriculture in using Drone, UAV and remote sensing. This paper summarizes the current state of three technologies for agricultural uses, including crop health monitoring and farm operations like, weed management, evapotranspiration estimation, spraying etc. In order to improve their agricultural production, more farmers should invest in contemporary equipment and technology, according to the research article's conclusion. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pilotless aircraft (drones) of copter type are able to solve numerous industrial problems very efficiently. Since mass-produced models do not always ensure efficient resource use, approaches to the modification and customization of mass-produced drones must be devised, tested, and introduced to improve their performance and productivity. A possible element of such a technology is considered in the present work, for the example of prototyping an asymmetric copter frame on the basis of reverse engineering and digital parametric modeling. [ABSTRACT FROM AUTHOR]

Development methods for pilotless aircraft (drones), which are currently of great interest, are analyzed. Spheres of human activity where such systems are used to solve infrastructure problems are noted. A development method is proposed; this method covers stages such as the development of an adjustable system composition, the development of a parameterized control structure, the creation of a digital twin, engineering analysis, generative design and topological optimization, the creation of a catalogued library of digital models for the primary and secondary equipment, and the development of design documentation. Approaches to the development of an adaptive subsystem for the pilotless system are outlined. [ABSTRACT FROM AUTHOR]

Copyright of Problems of Mechatronics. Armament, Aviation, Safety Engineering / Problemy Mechatroniki. Uzbrojenie, lotnictwo, Inżynieria Bezpieczeństwa is the property of Index Copernicus International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Qadisiyah Computer Science & Mathematics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring body length and body condition of individuals helps determine overall population health and assess adaptation to environmental changes. Aerial photogrammetry from drone‐based videos is a valuable method for obtaining body length and body condition measurements of cetaceans. However, the laborious manual processing of drone‐based videos to select frames to measure animals ultimately delays assessment of population health and hinders conservation actions. Here, we apply deep learning methods to expedite the processing of drone‐based videos to improve efficiency of obtaining important morphological measurements of whales. We develop two user‐friendly models to automatically (1) detect and output frames containing whales from drone‐based videos ("DeteX") and (2) extract body length and body condition measurements from input frames ("XtraX"). We use drone‐based videos of gray whales to compare manual versus automated measurements (n = 86). Our results show automated methods reduced processing times by one‐ninth, while achieving similar accuracy as manual measurements (mean coefficient of variation <5%). We also demonstrate how these methods are adaptable to other species and identify remaining challenges to help further improve automated measurements in the future. Importantly, these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health. [ABSTRACT FROM AUTHOR]

This paper presents a new rectenna system that combine a patch antenna with a solar cell to capture energy from both radio frequency (RF) signals and sunlight. The patch antenna collects RF signals, while the solar cell converts sunlight into electricity. This integration offers a sustainable energy solution for internet of things (IoT) sensors or drones. The antenna's performance at 26 GHz demonstrates impressive metrics, including a -68 dB S11 reflection, 700 MHz bandwidth, 6.25 dBi gain, 49.8 O impedance, and 42.25% RF-DC conversion efficiency. The "solar rectenna" integrates both technologies, driving technological advancement and fostering sustainability in wireless communication. [ABSTRACT FROM AUTHOR]

Aircraft carrier flight decks present high-risk mission-critical environments that need to be both efficient and safe. The concept of optimal manning, having just enough people to do the job safely and efficiently, is paramount in order to put the least amount of people at risk while not sacrificing mission effectiveness. To this end, an agent-based model, the optimal manning simulation (OMS) was developed, which specifically looks at the launch process of the flight deck in order to quantify the risk and efficiency of people working on the flight deck. OMS models different classes of crew members on the flight deck, aircraft, and resources like catapults. OMS measures safety through collisions or near-collisions of people and aircraft, as well as how long it takes to execute a launch cycle, the primary efficiency metric. Validation and sensitivity analyses provide confidence in OMS results. To demonstrate its utility, OMS is also used to predict how the future introduction of unmanned aerial vehicles could impact staffing and performance measures. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Context: Coastal beach environments provide habitats for marine megafauna, including turtles, rays, marine mammals and sharks. However, accessing these variable energy zones has been difficult for researchers by using traditional methods. Aims: This study used drone-based aerial surveys to assess spatio-temporal variation of marine megafauna across south-eastern Queensland, Australia. Methods: Drones were operated at five south-eastern Queensland beaches. Megafauna sightings and key variables including location, month and turbidity were analysed to assess variation across locations. Key results: Overall, 3815 individual megafauna were detected from 3273 flights. There were significant differences in the composition of megafauna assemblages throughout the year and among beaches, with megafaunal sightings in >80% of flights conducted off North Stradbroke Island. Conclusions: Strong temporal presence was found that is congruent with other studies examining seasonality. This supports the use of drones to provide ecological data for many hard-to-study megafauna species and help inform long-term sustainable management of coastal ecosystems. Implications: Results indicated that environmental conditions can influence the probability of sighting marine megafauna during aerial surveys. Studying beach environments by using boat-based methods has been difficult. Drone surveys were used to assess abundance patterns of large marine animals in south-eastern Queensland, Australia. Significant differences in the composition of animal groups were found throughout the year and among locations, with large marine animal sightings in >80% of flights conducted off North Stradbroke Island. This supports the use of drones to provide scientific data for hard-to-study marine species and inform management of coastal ecosystems. [ABSTRACT FROM AUTHOR]

"Biomimicry" is an acronym used to describe how people looked at nature for inspiration to tackle a variety of problems. The modern problems of fastincreasing urbanization, land degradation, climate change, pandemics, loss of biodiversity, and widespread use of pesticides and fertilizers seriously threaten our food supply chain. There is a growing consumer demand for nutrient-dense, flavourful plant-based cuisine with minimal environmental impact. Moreover, a considerable portion of food roughly 24% is lost before it reaches consumers, partly as a result of poor quality and protracted supply chains. Researching new methods of producing food is essential since, by 2050, there will be more than 9.7 billion people on the planet, 70% of whom will reside in cities. Vertical farming (VF), which relieves pressure on conventional agricultural land by using vertical space instead of horizontal expansion, is growing in popularity as a solution to these problems. Because VF incorporates soil-less growth techniques, it is well-suited for urban environments. This strategy may help to produce more premium products, such as fruits, vegetables, flowers, and herbs. It may also help to produce cosmetics and medications made from plants. Vertical farming, is becoming more favoured as an alternative to traditional agriculture, and provides avenues for enhancing sustainable food production given the growing challenges of climate change and population growth. [ABSTRACT FROM AUTHOR]

In rapidly changing ocean systems, there is a dual need to engage and educate community members and carry out rapid data acquisition. There is a body of evidence to support community or citizen science projects as successful vehicles for achieving these goals, with a particular need to increase global ocean literacy. The online SealSpotter program is a citizen science initiative aimed at monitoring trends in Australian fur seal (Arctocephalus pusillus doriferus) populations and connecting people to the marine environment. Here we present the findings of five years of monitoring of fur seals using drone surveys counted by citizen scientists via SealSpotter. Over five years, global participants from 23 countries were engaged in counting seals with a focus on the annual breeding season and pup abundance, with an average of 13,479 images and 171,137 seals counted per year. SealSpotter participants presented more conservative (lower) counts than expert counts, however both groups detected similar trends in abundance, emphasizing success of the project aims that included obtaining a precise index of pup abundance; ultimately a more achievable goal than accuracy due to the difficulties in measuring absolute abundance. We reflect on SealSpotter's accomplishments and highlight the potential for marine citizen science programs as important tools for addressing global ocean literacy needs. The SealSpotter program contributes to our understanding of marine ecosystems through a simple but effective citizen science program. [ABSTRACT FROM AUTHOR]

Background: The core vector control tools used to reduce malaria prevalence are currently long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS). These interventions are hindered by insecticide resistance and behavioural adaptation by malaria vectors. Thus, for effective interruption of malaria transmission, there is a need to develop novel vector control interventions and technologies to address the above challenges. Larviciding using drones was experimented as an innovative tool that could complement existing indoor interventions to control malaria. Methods: A non-randomized larviciding trial was carried out in irrigated rice fields in sub-urban Kigali, Rwanda. Potential mosquito larval habitats in study sites were mapped and subsequently sprayed using multirotor drones. Application of Bacillus thuringiensis var. israelensis (Bti) (Vectobac® WDG) was followed by entomological surveys that were performed every two weeks over a ten-month period. Sampling of mosquito larvae was done with dippers while adult mosquitoes were collected using CDC miniature light traps (CDC-LT) and pyrethrum spraying collection (PSC) methods. Malaria cases were routinely monitored through community health workers in villages surrounding the study sites. Results: The abundance of all-species mosquito larvae, Anopheles larvae and all-species pupae declined by 68.1%, 74.6% and 99.6%, respectively. Larval density was reduced by 93.3% for total larvae, 95.3% for the Anopheles larvae and 61.9% for pupae. The total adult mosquitoes and Anopheles gambiae sensu lato collected using CDC-Light trap declined by 60.6% and 80% respectively. Malaria incidence also declined significantly between intervention and control sites (U = 20, z = − 2.268, p = 0.023). Conclusions: The larviciding using drone technology implemented in Rwanda demonstrated a substantial reduction in abundance and density of mosquito larvae and, concomitant decline in adult mosquito populations and malaria incidences in villages contingent to the treatment sites. The scaling up of larval source management (LSM) has to be integrated in malaria programmes in targeted areas of malaria transmission in order to enhance the gains in malaria control. [ABSTRACT FROM AUTHOR]

Small unmanned aircraft systems (drones) have only been widely available for civilian application for less than 10 years. Despite the newness, drones are being used to support a broad range of public safety applications. The purpose of this study is to evaluate if law enforcement programs at two-year community colleges are providing adequate drone education. The course catalogs and websites of 255 community colleges in the Southeast United States were searched for drone-related keywords. Only 16% of the schools had at least one drone-related course available. A follow-up survey was sent to these schools' law enforcement program coordinators. The survey revealed that the program coordinators know that law enforcement is using drones, felt drones reduced injuries, and desired to expand their UAS curriculum. However, the survey also found that current faculty are unfamiliar with UAS regulations and have a general lack of professional development in this area. [ABSTRACT FROM AUTHOR]

Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture. [ABSTRACT FROM AUTHOR]

Remotely piloted aircraft (RPAs) have been increasingly used for crop protection in coffee plantations. However, the applications can result in low spray deposition on leaves and higher product losses between rows compared to ground airblast sprayers. This study aimed to evaluate the spray deposition on the coffee canopy and potential losses to the soil during application with an RPA and an airblast sprayer at varying spray volumes. The experiment comprised four spray treatments: RPA at 10 L ha−1 and 20 L ha−1, and airblast sprayer at 200 L ha−1 and 300 L ha−1. Leaf deposition was quantified by measuring a tracer on leaves from the lower and upper parts of the coffee canopy using spectrophotometry. Spray losses to the soil were measured by analyzing tracer residues on Petri dishes positioned within the inter-rows and beneath the coffee canopy. Statistical process control was used to analyze spray deposition quality in the study area. Ground-based airblast spraying resulted in the highest overall canopy deposition, while RPA spraying led to greater losses within the inter-rows. No significant difference was observed in spray runoff beneath the canopy between ground-based and aerial applications. Leaf deposition exhibited random variability across all application methods. Therefore, application stability, control, and spray quality standards were maintained. [ABSTRACT FROM AUTHOR]

Innovation documentation procedures are very necessary for the protection of cultural heritage sites in general. This study investigates the capabilities of 3D laser scanning and drone photogrammetry in saving accurate documenting and preserving the delicate features of architectural history. The objective of the study is to establish a definitive plan for choosing the optimal technology, either 3D laser scanning or drone photogrammetry, based on the specific needs and status of a restoration project. That’s by analysing the benefits and limitations of both technologies and investigating the potential for integrating them together. This research aims to determine the most suitable applications for each approach by evaluating their performance in terms of accuracy, resolution, data acquisition speed, and adaptation to varied environmental conditions. The results demonstrate that 3D laser scanning is highly effective in collecting intricate features, whereas drone photogrammetry provides quick coverage of large areas. The study's findings indicate that employing a combined strategy can greatly increase the quality and efficiency of architectural documentation. This, accordingly, leads to enhanced preservation efforts and deeper comprehension of cultural assets. [ABSTRACT FROM AUTHOR]